Charting the Growth of AI in Different Countries

Likhita Baswani , IIT Gandhinagar, likhita.b@iitgn.ac.in

Shridhar Pawar , IIT Gandhinagar, sominath.ps@iitgn.ac.in

Paras Jain , IIT Gandhinagar, jain.paras@iitgn.ac.in

from IPython.display import HTML
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from cProfile import label
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import bar_chart_race as bcr
from matplotlib import rc
rc('animation',html='jshtml')


df = pd.read_csv("data\publications_per_year.csv", index_col = "Year")
df.head()

	Afghanistan	Albania	Algeria	Angola	Argentina	Armenia	Australia	Azerbaijan	Bahrain	Bangladesh	...	United Arab Emirates	United Kingdom	United States	Uruguay	Uzbekistan	Venezuela	Viet Nam	Yemen	Zambia	Zimbabwe
Year
1980	0.0	1.0	0.0	0.0	19.500000	0.0	498.716667	1.0	1.500	0.000000	...	12.45	1708.472342	10746.88088	0.0	0.0	7.166667	0.0	0.0	2.0	1.000000
1981	0.0	0.0	0.0	0.5	19.500000	2.0	499.537121	0.0	3.000	2.300000	...	22.25	1697.864286	11740.56502	0.0	0.0	7.250000	0.2	0.0	4.0	1.500000
1982	0.0	0.0	1.0	1.0	13.066667	1.0	542.394444	1.0	7.375	1.000000	...	20.25	1955.949242	12438.06136	1.0	1.0	11.900000	0.0	0.0	0.0	4.000000
1983	0.0	0.0	1.0	1.0	13.066667	1.0	542.394444	1.0	7.375	1.000000	...	20.25	1955.949242	12438.06136	1.0	1.0	11.900000	0.0	0.0	0.0	4.000000
1984	0.0	0.0	0.0	2.0	25.883333	4.0	556.416667	0.0	3.000	2.611111	...	21.50	2253.234475	14208.50701	0.0	0.0	11.166667	0.0	0.5	1.0	2.333333

5 rows × 131 columns

# using the bar_chart_race package
bcr.bar_chart_race(
    # DataFrame with each row representing a single period of time.
    df = df,
    
    # Figure properties
    fig_kwargs = {
        'figsize': (8, 3),
        'dpi': 120,
        'facecolor': '#F8FAFF'
    },

    # Horizontal orientation of bars
    orientation = "h",

    # Bars sorted in descending order
    sort = "desc",
 
    # Number of Bars in Each Frame
    n_bars = 10,
    
    # Fixing the Maximum Value of x-axis
    # fixed_max = True,

    # Animation Smoothness
    steps_per_period = 45,

    # Time Period in ms for Each Row
    period_length = 1500,

    # Custom Colors
    colors=[
        '#6ECBCE', '#FF2243', '#FFC33D', '#CE9673', '#FFA0FF', '#6501E5', '#F79522', '#699AF8', '#34718E', '#00DBCD',
        '#00A3FF', '#F8A737', '#56BD5B', '#D40CE5', '#6936F9', '#FF317B', '#0000F3', '#FFA0A0', '#31FF83', '#0556F3'
    ],

    # Title
    title = 'Top 10 Countries with Maximum Number of Publications 1980-2021',

    # Adjusting Opacity and Width of Bars
    bar_kwargs={'alpha': .90, 'lw': 0},

    # Adjusting the Bar Label Format
    bar_texttemplate='{x:.0f}',

    # Adjusting the Period Label Format
    period_template='{x:.0f}',
    # filename='bi.mp4'

)

import matplotlib.pyplot as plt

import plotly.express as px
# df2=pd.read_csv("AI publication time series by country.csv")
# df2.head()
# df2 = px.data.gapminder().query("Country=='India'")
# # fig = px.line(df2, x="Year", y="Value", title='Papers in India')
# # fig.show()
df2 = pd.read_csv("data\publications_per_year.csv")
df2.head()
values_usa=df2['United States']
values_china=df2['China']
values_india=df2['India']
values_eu27=df2['EU (27)']
values_uk=df2['United Kingdom']
values_brazil=df2['Brazil']
years=df2['Year']
plt.figure(figsize=(15,9))
plt.plot(years,values_usa,marker='*',markersize=7, label='USA')
plt.plot(years,values_china,marker='*',markersize=7, label='China')
plt.plot(years,values_india,marker='*',markersize=7, label= 'India')
plt.plot(years,values_eu27,marker='*',markersize=7, label= 'EU (27)')
plt.plot(years,values_uk,marker='*',markersize=7, label= 'United Kingdom')
plt.plot(years,values_brazil,marker='*',markersize=7, label= 'Brazil')
plt.xlabel('Years')
plt.ylabel('No of AI Research Publications')
plt.title('No of AI Research Publications vs Years')
plt.grid(True)
plt.legend()
plt.show()

df3=pd.read_csv("data\AI research by institution.csv")
df3.head()

	Year	Chinese Academy of Sciences	Tsinghua University	Massachusetts Institute of Technology	Stanford University	Shanghai Jiao Tong University	University of Michigan	Harvard University	Max Planck Society	Zhejiang University	Carnegie Mellon University
0	1980	1.000000	1.000000	204.833333	202.552020	0.000000	130.817100	128.492857	83.000000	0.000000	111.338095
1	1981	3.166667	5.083333	240.991667	221.825000	0.000000	148.477381	169.448413	90.566667	2.000000	134.969697
2	1982	2.833333	3.000000	253.833333	232.009957	3.500000	170.967857	154.605988	96.816667	0.333333	106.000000
3	1983	8.166667	7.000000	275.039827	219.934524	3.500000	171.625000	189.644877	110.693831	1.000000	171.009524
4	1984	5.500000	5.000000	270.991667	251.114286	2.833333	223.785714	172.583333	102.987989	0.500000	182.283333

years=df3['Year']
# years
values_chinese=df3['Chinese Academy of Sciences']
values_tsinghua=df3['Tsinghua University']
values_massachusetts=df3['Massachusetts Institute of Technology']
values_stanford=df3['Stanford University']
values_maxplanck=df3['Max Planck Society']
values_harvard=df3['Harvard University']

plt.figure(figsize=(15,9))
plt.plot(years,values_chinese,marker='*',markersize=7, label='Chinese Academy of Sciences')
plt.plot(years,values_tsinghua,marker='*',markersize=7, label='Tsinghua University')
plt.plot(years,values_massachusetts,marker='*',markersize=7, label= 'Massachusetts Institute of Technology')
plt.plot(years,values_stanford,marker='*',markersize=7, label= 'Stanford University')
plt.plot(years,values_maxplanck,marker='*',markersize=7, label= 'Max Planck Society')
plt.plot(years,values_harvard,marker='*',markersize=7, label= 'Harvard University')
plt.xlabel('Years')
plt.ylabel('No of AI Research Publications of Different Universities')
plt.title('No of AI Research Publications vs Years')
plt.grid(True)
plt.legend()
plt.show()

# import CountryCode
CountryCode=pd.read_csv('https://pkgstore.datahub.io/JohnSnowLabs/country-and-continent-codes-list/country-and-continent-codes-list-csv_csv/data/b7876b7f496677669644f3d1069d3121/country-and-continent-codes-list-csv_csv.csv',sep=',')
CountryCode

data=pd.read_csv('data\GDPvsPapers.csv')
data.head()

	Year	Country	no of AI Research Publication	GDP Per Capita	Total number of number of All Research Publication
0	1980	AFG	0.0	272.655510	0.0
1	1981	AFG	0.0	264.111197	0.0
2	1982	AFG	0.0	260.078596	0.0
3	1983	AFG	0.0	256.045995	0.0
4	1984	AFG	0.0	252.013395	1.0

CountryCode=CountryCode[['Continent_Name','Three_Letter_Country_Code']]
df_final=pd.merge(data,CountryCode, left_on='Country',right_on='Three_Letter_Country_Code',how='left')
df_final = df_final.iloc[:, :-1] #remove last column
df_final['Continent_Name'] = df_final['Continent_Name'].replace(np.nan, 'European Union')
df_final

	Year	Country	no of AI Research Publication	GDP Per Capita	Total number of number of All Research Publication	Continent_Name
0	1980	AFG	0.000000	272.655510	0.000000	Asia
1	1981	AFG	0.000000	264.111197	0.000000	Asia
2	1982	AFG	0.000000	260.078596	0.000000	Asia
3	1983	AFG	0.000000	256.045995	0.000000	Asia
4	1984	AFG	0.000000	252.013395	1.000000	Asia
...	...	...	...	...	...	...
6757	2017	ZWE	143.498430	1335.665064	1081.028519	Africa
6758	2018	ZWE	140.172395	1352.162653	1088.801098	Africa
6759	2019	ZWE	146.150396	1156.154864	1161.211884	Africa
6760	2020	ZWE	176.921210	1128.210711	1392.711900	Africa
6761	2021	ZWE	165.467014	1128.210711	1354.236918	Africa

6762 rows × 6 columns

import matplotlib.pyplot as plt
import plotly.express as px
import plotly.io as pio
pio.renderers.default='notebook_connected'
fig = px.scatter(df_final,x='no of AI Research Publication', y='GDP Per Capita',animation_frame='Year', 
 animation_group='Country',size='Total number of number of All Research Publication', 
 color='Continent_Name',
 hover_name='Country', text='Country',
 size_max=45,range_x=[0,150000], range_y=[0,110000])
fig.layout.updatemenus[0].buttons[0].args[1]['frame']['duration'] = 700
textfont=dict(
        family="sans serif",
        size=18,
        color="LightSeaGreen"
    )
fig.update_layout(uniformtext_minsize=10, uniformtext_mode='hide')
# fig.update_traces(textfont_size=5)
# fig.update_traces(textposition='inside', textfont_size=14)
fig.show(renderer='notebook_connected')

df4 = px.data.gapminder()
fig = px.scatter(df_final, x = 'GDP Per Capita', y = 'no of AI Research Publication' ,animation_frame = 'Year', 
                 animation_group = 'Country', size = 'Total number of number of All Research Publication', color = 'Continent_Name', hover_name = 'Country', 
                 facet_col = 'Continent_Name', log_x=True, size_max=45, range_x=[10,110000], range_y=[0,150000]) 

fig.update_layout(
 title='No. of AI Publications vs Per Capita GDP, 1980-2021',
 yaxis=dict(
 title='No. of AI Publications',
 gridcolor='white',
 gridwidth=2,
 ))
fig.for_each_annotation(lambda a: a.update(text=a.text.split('=')[-1]))
for i in range(1,8):
 fig.update_xaxes(title_text='GDP/Capita', row=1, col=i)
fig.show()